显然没有偏见的大型语言模型仍然形成有偏见的关联.
Xuechunzi Bai1, Angelina Wang2, Ilia Sucholutsky3
1Department of Psychology, The University of Chicago, Chicago, IL 60637.
概括
大型语言模型 (LLM) 显示隐含的社会偏见,尽管通过了明确的测试. 基于提示的新方法揭示了人工智能决策中的这些微妙偏见,反映了社会刻板印象.
科学领域:
- 人工智能的人工智能
- 心理学 心理学 心理学
- 社会学 社会学 社会学
背景情况:
- 大型语言模型 (LLM) 可以表现出隐含的社会偏见,类似于人类,即使通过了明确的偏见测试.
- 测量LLM中的隐性偏差是具有挑战性的,因为专有模型和需要评估对决策的影响.
研究的目的:
- 引入新的基于提示的措施,以检测LLMs中的隐性偏见.
- 评估价值调整的LLMs中隐含的刻板印象偏见的存在和性质.
主要方法:
- 开发了LLM词汇关联测试,调整了隐性关联测试以用于基于提示的偏差检测.
- 引入了LLM相对决策测试,以评估情境AI决策中的微妙歧视.
- 将这些方法应用于跨种族,性别,宗教和健康类别的8个价值调整的LLM.
主要成果:
- 在测试的LLM中发现了普遍的刻板印象偏见,反映了社会偏见.
- 在21种特定的刻板印象中发现了偏见,包括与种族,性别,年龄和健康的关联.
- 基于提示的措施成功地揭示了专有LLM中的细微偏差,而标准基准没有发现.
结论:
- 基于提示的新型心理措施可以有效地揭示LLMs中的隐含偏见.
- 即使是那些与价值观一致的LLM,也存在影响其决策的社会偏见.
- 这些发现强调了需要先进的方法来确保AI公平性和减轻有害的刻板印象.
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